Data Science Leadership

Using R to Drive Agility in Clinical Reporting: Questions and Answers

2020-10-08 Andy Nicholls, Michael Rimler
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Andy Nicholls and Michael Rimler from healthcare firm GlaxoSmithKline plc (GSK) answer questions posed during their recent webinar, Using R to Drive Agility in Clinical Reporting. Read more →

RStudio Named Strong Performer in the Forrester Wave™: Notebook-Based Predictive Analytics and Machine Learning, Q3 2020

2020-09-25 Lou Bajuk
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Read The Forrester Wave™: Notebook-Based Predictive Analytics and Machine Learning, Q3 2020 Report to learn why RStudio was named a Strong Performer by this independent research firm, and received the highest scores possible in the evaluation criteria of security, apps, open source and platform infrastructure. Read more →

Ease Uncertainty by Boosting Your Data Science Team's Skills

2020-09-23 Carl Howe
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To help address some of the uncertainty data science leaders may be feeling heading into the fall planning season, we note three new resources to help your team learn new skills and communicate their value better. Read more →

Debunking R and Python Myths: Answering Your Questions

2020-09-10 Samantha Toet and Carl Howe
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In this post, we answer questions raised by participants and attendees during our recent Debunking R & Python Myths webinar. Our bottom line was to use the tools that let you be most productive in the shortest amount of time. Read more →

3 Ways to Expand Your Data Science Compute Resources

2020-08-27 Carl Howe
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Data scientists frequently have computational needs that stretch far beyond their laptops. Data science leaders should embrace features of RStudio Server that give data scientists access to shared IT resources without breaking the bank Read more →

Why Package and Environment Management is Critical for Serious Data Science

2020-08-20 Mike Garcia, ProCogia
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The renv package helps create reproducible project environments that are critical for data science teams to deliver real, lasting value. Read more →

R and RStudio - The Interoperability Environment for Data Analytics

2020-08-17 Curtis Kephart and Lou Bajuk
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From design philosophies to current development priorities, R with RStudio is a wonderful environment for anyone who seeks understanding through the analysis of data. Here's why. Read more →

Do, Share, Teach, and Learn Data Science with RStudio Cloud

2020-08-05 Lou Bajuk
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RStudio is proud to announce the general availability of RStudio Cloud, its cloud-based platform for doing, teaching, and learning data science. WIth RStudio Cloud, there's nothing to configure and no dedicated hardware or installation required. Individual users, instructors, and students only need a browser. Read more →

3 Wild-Caught R and Python Applications

2020-07-28 Carl Howe
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In this post I present three "wild-caught" examples solicited from the R community of how they use interoperability between R, Python and other languages to solve real-world problems. Read more →

Interoperability: Getting the Most Out of Your Analytic Investments

2020-07-15 Lou Bajuk, Carl Howe
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No single platform meets all the analytic needs of every organization. To avoid productivity-sapping complexity and underutilized infrastructure, encourage Interoperability so that your data scientists can access everything they need from their native tools. Read more →